Giacomo Bormetti, Department of Mathematics, University of Bologna
Date: 12 JULY 2018 from 14:30 to 16:00
Event location: Aula Seminari, 1st floor, Department of Statistica, Via Belle Arti 41, Bologna
Type: Statistics Seminars
Abstract:
By interpreting score-driven models of Creal et al. (2013) and Harvey (2013) as approximate filters, we introduce a new class of simple approximate smoothers for nonlinear non-Gaussian state-space models that are named "Score-Driven Smoothers" (SDS). The newly proposed SDS improves on standard score-driven filtered estimates, as it employs all available observations. In contrast to complex simulations-based methods, the SDS has similar structure to Kalman backward smoothing recursions but uses the score of the non-Gaussian density. Through an extensive Monte Carlo study, we provide evidence that the performance of the approximation is very close to that of simulation-based techniques, while at the same time requiring significantly lower computational burden.
Joint work with Giuseppe Buccheri, Fulvio Corsi, and Fabrizio Lillo
L’Organizzatore Il Direttore
Prof. Monia Lupparelli Prof. Angela Montanari
La S.V. è invitata